Preprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer disease

DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer

GABAergic and Cortical and Subcortical Glutamatergic Axon Terminals Contain CB1 Cannabinoid Receptors in the Ventromedial Nucleus of the Hypothalamus

Background: Type-1 cannabinoid receptors (CB1R) are enriched in the hypothalamus, particularly in the ventromedial hypothalamic nucleus (VMH) that participates in homeostatic and behavioral functions including food intake. Although CB1R activation modulates excitatory and inhibitory synaptic transmission in the brain, CB1R contribution to the molecular architecture of the excitatory and inhibitory synaptic terminals in the VMH is not known. Therefore, the aim of this study was to investigate the precise subcellular distribution of CB1R in the VMH to better understand the modulation exerted by the endocannabinoid system on the complex brain circuitries converging into this nucleus. Methodology/Principal Findings: Light and electron microscopy techniques were used to analyze CB1R distribution in the VMH of CB1R-WT, CB1R-KO and conditional mutant mice bearing a selective deletion of CB1R in cortical glutamatergic (Glu-CB1R-KO) or GABAergic neurons (GABA-CB1R-KO). At light microscopy, CB1R immunolabeling was observed in the VMH of CB1R-WT and Glu-CB1R-KO animals, being remarkably reduced in GABA-CB1R-KO mice. In the electron microscope, CB1R appeared in membranes of both glutamatergic and GABAergic terminals/preterminals. There was no significant difference in the percentage of CB1R immunopositive profiles and CB1R density in terminals making asymmetric or symmetric synapses in CB1R-WT mice. Furthermore, the proportion of CB1R immunopositive terminals/preterminals in CB1R-WT and Glu-CB1R-KO mice was reduced in GABA-CB1R-KO mutants. CB1R density was similar in all animal conditions. Finally, the percentage of CB1R labeled boutons making asymmetric synapses slightly decreased in Glu-CB1R-KO mutants relative to CB1R-WT mice, indicating that CB1R was distributed in cortical and subcortical excitatory synaptic terminals. Conclusions/Significance: Our anatomical results support the idea that the VMH is a relevant hub candidate in the endocannabinoid-mediated modulation of the excitatory and inhibitory neurotransmission of cortical and subcortical pathways regulating essential hypothalamic functions for the individual's survival such as the feeding behavior.

Brand image and lexicographic analysis: an application to shopping malls

[EN] The concept of image in its different aspects is very important in today s society as well as in the business management field. Some authors reports that most of the studies that measure image do not take into account neither previous theoretical and conceptual models nor other possible empirical evidence alternatives. Given this need, a research regarding the concept of brand image applied to shopping malls was conducted based on the conceptual model of the consumer cognitive response in order to empirically explore and contrast it. For this reason, a survey was applied to 420 consumers in five shopping malls in Bogotá, achieving a database of 3.749 cases. The results show attribute-shopping mall associations expressed in unique, differentiated, and notorious vocabulary obtained applying lexicometric and multivariate analysis techniques. Attribute-shopping mall associations such as spacious , good location , good variety of stores , and the existence of movie theaters . Finally, this research aims to potentially improve the management of shopping malls and increase their attractiveness and customer loyalty by applying the development of service quality systems, integral communication, segmentation, and positioning.

Analysis, optimization and development of an answer scoring system

The main contribution of this work is to analyze and describe the state of the art performance as regards answer scoring systems from the SemEval- 2013 task, as well as to continue with the development of an answer scoring system (EHU-ALM) developed in the University of the Basque Country. On the overall this master thesis focuses on finding any possible configuration that lets improve the results in the SemEval dataset by using attribute engineering techniques in order to find optimal feature subsets, along with trying different hierarchical configurations in order to analyze its performance against the traditional one versus all approach. Altogether, throughout the work we propose two alternative strategies: on the one hand, to improve the EHU-ALM system without changing the architecture, and, on the other hand, to improve the system adapting it to an hierarchical con- figuration. To build such new models we describe and use distinct attribute engineering, data preprocessing, and machine learning techniques.

"Heroes" and "Villains" of World History across Cultures

Emergent properties of global political culture were examined using data from the World History Survey (WHS) involving 6,902 university students in 37 countries evaluating 40 figures from world history. Multidimensional scaling and factor analysis techniques found only limited forms of universality in evaluations across Western, Catholic/Orthodox, Muslim, and Asian country clusters. The highest consensus across cultures involved scientific innovators, with Einstein having the most positive evaluation overall. Peaceful humanitarians like Mother Theresa and Gandhi followed. There was much less cross-cultural consistency in the evaluation of negative figures, led by Hitler, Osama bin Laden, and Saddam Hussein. After more traditional empirical methods (e.g., factor analysis) failed to identify meaningful cross-cultural patterns, Latent Profile Analysis (LPA) was used to identify four global representational profiles: Secular and Religious Idealists were overwhelmingly prevalent in Christian countries, and Political Realists were common in Muslim and Asian countries. We discuss possible consequences and interpretations of these different representational profiles.